]]>Greetings all. Many of you many know that I’m a featured guest this weekend at the Re-Find Health Lecture Series in London. This same series was also scheduled to happen in Stockholm, but has now been cancelled by Re-Find Health. I regret the cancellation, so I’m organizing a (free) Facebook Live event – details to be announced soon. I realize this isn’t the same as being there live, but hopefully this makes the cancellation a little less disappointing. I will be reaching out to friends and followers in Stockholm and Sweden to invite questions and participation in advice – and will prioritize these during the Facebook Live event.
]]>1148Stockholm and London Talks and Dinners, Postponed, a quick notehttp://garytaubes.com/stockholm-and-london-talks-and-dinners-postponed-a-quick-note/
Mon, 23 Oct 2017 19:44:58 +0000http://garytaubes.com/?p=1122

]]>Much to my dismay, we’ve had to postpone the Stockholm (November 16) and London (No 18&19) events. We’ve tentatively rescheduled Stockholm for March 15th and London for March 17th and 18th and will finalize those dates as quickly as we can. I apologize for any inconvenience this causes folks. Tickets can be kept for the March events (although keep in mind dates are not yet locked), transferred to another Re-Find Health event, or refunded via an e-mail request to Re-Find Health. Again, my apologies for any inconvenience. We’ll make this work yet.
]]>1122Vegetable oils, (Francis) Bacon, Bing Crosby, and the American Heart Associationhttp://garytaubes.com/vegetable-oils-francis-bacon-bing-crosby-and-the-american-heart-association/
Sat, 17 Jun 2017 19:15:22 +0000http://garytaubes.com/?p=1112

The human understanding, once it has adopted opinions, either because they were already accepted and believed, or because it likes them, draws everything else to support and agree with them. And though it may meet a greater number and weight of contrary instances, it will, with great and harmful prejudice, ignore or condemn or exclude them by introducing some distinction, in order that the authority of those earlier assumptions may remain intact and unharmed. –Francis Bacon, Novum Organum, 1620

Four hundred years ago, give or take a couple of years, Francis Bacon pioneered thinking about the scientific method by noting that humans are programmed to pay more attention to evidence that agrees with their preconceptions and to reject evidence that doesn’t, and that this thinking leads to very effective religious dogma but not to reliable knowledge of the universe. Hence what was needed was a new technology of reasoning –Novum Organum, per the title of his book in Latin – that would minimize these tendencies, although he recognized that getting rid of them entirely was not an option. Humans didn’t function that way.

In good science, this kind of cognitive bias is addressed, among other methodologies, by deciding in advance of looking at the evidence (or doing a trial) what criteria will be used to judge the worth of the evidence (the results of the trial) without knowledge of whether that evidence supports our hypotheses. This is one reason why clinical trials are done double blind, and the data analyzed by researchers who are blinded to whether the subjects were interventions or controls, such that the biases of the investigators (or even the subjects) don’t bias the interpretation of the results.

For whatever reason, when it comes to heart disease and dietary fat, the investigators whom the American Heart Association chooses to determine what we should or should not eat have never been believers in this kind of, well, scientific methodology. This was the general conclusion of my first investigation into the dietary fat story going on 20 years ago for the journal Science. I’d like to say the situation has improved, but clearly it hasn’t. The latest Presidential Advisory from the AHA on saturated fat is the AHA’s expert authorities – what Inspector Renaud in Casablanca would have called “the usual suspects” – reiterating that they were right fifty years ago, and they were right 20 years ago, and they’re still right. And the techniques they used to come to those conclusions can be used again and again until someone stops them. Which is unlikely to happen.

A Scottish cardiologist/epidemiologist described this pseudoscientific methodology to me as “Bing Crosby epidemiology” – i.e., “accentuate the positive and eliminate the negative.” In short, it’s cherry picking, and it’s how a lawyer builds an argument but not how a scientist works to establish reliable knowledge, which is the goal of the enterprise. Not winning per se, but being right. It’s why I wrote in the epilogue of my first book on nutrition, Good Calories, Bad Calories, that I didn’t consider these people doing research in the nexus of diet, obesity and disease to be real scientists. They don’t want to know the truth; they only wanted to convince maybe themselves and certainly the rest of us that they already do and have all along. While all good science requires making judgments about what evidence is reliable and what isn’t, scientists have to do this keeping in mind that the first principle of good science, now quoting Feynman, “is that you must not fool yourself and you’re the easiest person to fool.” The history of science is littered with failed hypotheses based on selective interpretation of the evidence. Regrettably the AHA experts simply don’t believe that what’s true of far better scientists then themselves, could possibly be true of them as well.

Today’s Presidential Advisory, written by a dozen esteemed experts led by Harvard’s Frank Sacks, may be the most egregious example of Bing Crosby epidemiology that I’ve ever seen. It’s particularly interesting because four years ago the AHA released a report claiming to be evidence-based medicine, co-authored by an intersecting set of these usual suspects, that also claimed that the strongest possible evidence existed to restrict the saturated fat (SFA) in our diet and replace it with polyunsaturates (PUFAs). It was fascinating because multiple other meta-analyses, co-authored by independent researchers, had found the evidence to be weak or lacking. So how could it be the strongest possible? Surely there was room for improvement. That 2013 AHA document, though, made it exceedingly difficult to duplicate the analysis of the AHA experts and establish how they had come to such a paradoxical conclusion. This latest document in effect tells us what they did then and are still doing – i.e., what they’ve been doing all along.

Whether consciously or unconsciously, they assume that what they think is true surely is, and then they methodically eliminate the negative and accentuate the positive until they can make the case that they are surely, clearly and unequivocally right. And they might be, just as a lawyer arguing a case to a jury might always be right, but you can never know it from the lawyer’s argument alone. You have to hear the counter as well and then maybe you can decide.

So let’s look at this process of eliminating the negative: the AHA concludes that only four clinical trials have ever been done with sufficiently reliable methodology to allow them to assess the value of replacing SFAs with PUFAs (in practice replacing animal fats by vegetable oils) and concludes that this replacement will reduce heart attacks by 30 percent. In the history of this debate, this is a huge, if not unprecedented number. These four trials are the ones that are left after the AHA experts have systematically picked through the others and found reasons to reject all that didn’t find such a large positive effect, including a significant number that happened to suggest the opposite. For these trials they carefully identify why these trials were critically if not fatally flawed, and so why their results cannot and should not be used in any reasonable assessment. As Bacon might have said, “with great and harmful prejudice [the AHA experts] ignore or condemn or exclude them by introducing some distinction, in order that the authority of [their] earlier assumptions may remain intact and unharmed.”

They do this for every trial but the four, including among the rejections the largest trials ever done: the Minnesota Coronary Survey, the Sydney Heart Study, and, most notably, the Women’s Health Initiative, which was the single largest and most expensive clinical trial ever done. All of these resulted in evidence that refuted the hypothesis. All are rejected from the analysis. And the AHA experts have good reasons for all of these decisions, but when other organizations – most notably the Cochrane Collaboration – did this exercise correctly, deciding on a strict methodology in advance that would determine which studies to use and which not, without knowing the results, these trials were typically included.

What the AHA experts don’t do (perhaps because they are convinced they can’t possibly be fooling themselves) is make the same effort with the trials that do support their hypothesis and assumptions. If they did, they make little indication of it. Of the four studies that support the 30 percent reduction, all are ancient by the standards of nutrition science. All date to the 1960s. One of them, for instance, is the Oslo Diet-Heart Study. This trial reported a significant reduction in CVD events, in line with the beliefs of the AHA authors, and so it’s included among the four trials considered worthy of making the cut. The Oslo trial was indeed typical for the era, which means very primitive by today’s standards. A single investigator, Paul Leren, has local physicians recommend to him for inclusion in the study patients who are at high risk of heart disease or have already had heart attacks. He randomizes half of these patients, now subjects, to eat a low-SFA, high PUFA diet and then gives them intensive counseling for years (“continuous instruction and supervision,” as Leren puts it), and he compares them to a control group that gets no counseling and eats the standard Norwegian diet.

So one group gets a “healthy” diet and intensive counseling for years; the other group gets nothing. Nada. This is technically called performance bias and it’s the equivalent of doing an unblinded drug trial without a placebo. It is literally an uncontrolled trial, despite the randomization. (In this case, as Leren explains, all the physicians involved also knew whether their patients were assigned to the intervention group or the control, which makes investigator bias all that much more likely.) We would never accept such a trial as a valid test of a drug. Why do it for diet? Well, maybe because it can be used to support our preconceptions, but that’s not really a good answer. I’m guessing that the AHA experts made no attempt to find out if this trial was worthy of rejection because they liked the result If I’m wrong, I apologize and I hope one of them will write to tell me.

Why do I know this about Oslo? Because I was curious, always a good thing, and, of course, because it disagrees with my preconceptions and my biases. Still, my curiosity could not be satisfied by reading the published literature because Leren didn’t give the necessary details in the published studies. He probably didn’t have the space. He did in a monograph he published in 1966. I bought a copy a few weeks ago. That’s how curious I was. It’s in this monograph that Leren assesses the state of the science, just as our AHA experts do now, fifty-one years later, and he then describes in pretty good detail what he actually did in the trial. He also discusses the dietary changes achieved in his intervention group, and here’s where the performance bias, rather than the PUFA/SFA shift, may have determined the study outcome.

Leren mentions in passing that sugar consumption in his intervention group was very low, about 50 grams a day, which is 40 pounds a year and is probably less than half of the per capita consumption in Norway in that era. (I’m extrapolating back from this data — i.e., guessing.) So this is a critical problem with performance bias in a diet study, any diet study. As we’re taught in eight grade science classes, good scientific experiments change a single variable with an intervention such that we can see the effect of that change. In this trial, the variable that’s supposed to be different is the SFA/PUFA ratio, but the performance bias introduces another one. One group gets continuous counseling to eat healthy, one group doesn’t. Now how can that continuous counseling influence health status? One way is that apparently the group that got it decided to eat a hell of lot less sugar. This unintended consequence now gives another possible explanation for why these folks had so many fewer heart attacks. I don’t know if this is true. The point is neither did Leren. And neither do our AHA authorities. Although we can speculate that had they decided in advance what criteria they would use to reject studies and then have the studies assessed blindly, such that the individuals making the choice had no knowledge of the results of the study, they would have rejected this one, too. And the others, as well. All of the four studies used to support the 30 percent number had significant flaws, often this very same performance bias. Reason to reject them.

The PrediMed trial is another good example of the AHA’s Bing Crosby epidemiology. The AHA authorities, as they say in passing, would like us to eat a Mediterranean diet, and so they conclude the evidence from PrediMed supports this advice. PrediMed may be the most influential clinical trial of the last decade, but it, too, was critically flawed. No, fatally flawed. You had to read the supplemental data in this case to find out. The researchers randomized subjects to three arms, one of which got nuts (Mediterranean) and regular counseling; one got olive oil (Mediterranean) and counseling; one (non-Mediterranean) got bupkus and no counseling. Hence, significant performance bias. Midway through the trial, the researchers actually realize that this was a problem and decide to address it. Here’s how they describe this revelation on page 10 of the supplemental material:

The initial dietary protocol for the Control group started with the delivery of a leaflet summarizing the recommendations to follow a low-fat diet (Table S2-S3) and scheduled one yearly visit. In October 2006, 3 years into the trial, we realized that such a low-grade intervention might potentially represent a weakness of the trial and amended the protocol to include quarterly individual and group sessions with delivery of food descriptions [my italics] shopping lists, meal plans and recipes (adapted to the low-fat diet) in such a way that the intensity of the intervention was similar to that of the Mediterranean diet groups, except for the provision of supplemental foods for free. This amendment of the protocol in no way meant a change in the quality and specific goals of the recommendations to the control group; it was only an enhancement in the eagerness of the intervention to make it similar to that delivered to participants in the Mediterranean diet groups.

Sound of throat clearing… Imagine a drug trial, in which “three years into the trial” the investigators realize that it might be a problem that they neglected to give the control group a placebo. Oops. Would editors of a prestigious journal buy the idea that “such a low-grade intervention might [might!!] potentially represent a weakness of the trial?” Would such a trial get published in any respectable journal? In nutrition, and because the cognoscenti in the nutrition community like the results, it’s published in the New England Journal of Medicine, the most prestigious medical journal in the world, and makes it to the front page of the New York Times. And the admission of this potential weakness is only made in the supplemental material. Not in the paper itself. Imagine had the study found that the Mediterranean diet was actually harmful. That giving nuts and olive oil increased the risk of death. Do you think the assembled experts of the AHA would have included it in this assessment, or would they have found this performance bias problem and rejected it on that basis? I’m voting for the latter, but we’ll never know.

Ultimately this AHA document is a recapitulation of what the AHA experts have been arguing for decades. The only reason to publish it is because it’s been taken heat lately from folks like me and Nina Teicholz and a host of others who point out that we’re dealing with a pseudoscience here and the public deserves far better. Those of us who have become critics may indeed be biased about what we believe now – I certainly am — but ultimately we’re arguing for better science. This kind of post-hoc analyses of clinical trials, whether subgroup analysis or otherwise, can only be hypothesis generating. That’s basic logic. We don’t have to take a vote. Just open a basic science or biostatistics textbook. What the AHA experts are doing here is saying that their assessment of the data leads to what they consider a compelling hypothesis: replacing SFA with PUFA should reduce heart disease by 30 percent. But that’s all they can say. By deciding what data to include and what not based on their preconceptions of what’s true and what’s not, they cannot say this is a fact, as they claim, only that it’s still a reasonable hypothesis and has yet to be refuted.

This leads to three further critical points.

1. One reason why the AHA’s four favored trials were done in the 1960s was not just to see if exchanging SFA for PUFA reduced heart disease risk, but to see if it reduced mortality. Like any drug, it’s not enough to show that an intervention has positive effects, benefits, you have to demonstrate that those benefits counterbalance the negatives, the risks. In the 1960s, the researchers and the public health authorities understood that and so most of these trials looked at total mortality as an endpoint. Only the Finnish Mental Hospital study showed a benefit of the diet on longevity and that was only in men. Not in women. (In fact, all the trials used to establish the 30 percent reduction number were done only in men. The Women’s Health Initiative was done in large part to see if what might be true for men would also be true for women, but the AHA doesn’t like this study, so we’re stuck with all men.)

Recently the epidemiologists discussing dietary fats and disease have taken again to focusing only on CHD, but they don’t say why. Even the Cochrane meta-analyses focus only on heart disease. My guess is they do this because the clinical trials showed no benefit for total mortality (they were mostly underpowered) and total mortality is hopelessly confounded in the observational studies. Personally, I’d rather die of heart disease than cancer or Alzheimer’s, but that’s may be because my familial experience has been with cancer and Alzheimer’s and it wasn’t pretty. Either way, if I’m going to change my diet and start consuming vegetable oils I want to know if I’m going to live longer. The AHA doesn’t even address that question. The first rule of medicine, preventive or otherwise, is still do no harm, and they’re making no attempt to assess harm. You can argue that they’re the AHA so what they care about is heart disease. But it’s not good enough. It’s never been good enough. And this leads to the second point.

2. The AHA experts do acknowledge that they’re discussing the same decades-old trials that we’ve been arguing about for, well, decades, and they do acknowledge implicitly that these trials cannot resolve this controversy, and then they state explicitly what would be necessary to do so:

The core trials reviewed in this section were started in the late 1950s and early 1960s. Readers may wonder why at least 1 definitive clinical trial has not been completed since then. Reasons include the high cost of a trial having upward of 20 000 to 30 000 participants needed to achieve satisfactory statistical power, the feasibility of delivering the dietary intervention to such a large study population, technical difficulties in establishing food distribution centers necessary to maintain high adherence for at least 5 years, and declining CVD incidence rates caused by improved lifestyle and better medical treatment [my italics]. These linked issues, which must be managed to obtain a definitive result, remain the central considerations for dietary trials on CVD and indeed are the overarching reason why few of these trials have ever been done. Finally, by the 1980s, with rising rates of breast and colon cancer, the US government committed to conducting the WHI (Women’s Health Initiative), a trial that studied a diet aimed at decreasing total fat in the diet to 20% with the expectation that saturated fat would likewise be substantially decreased. Consequently, carbohydrates were increased in the diet. Details are discussed subsequently.

So a rigorous test probably can’t be done. And, more importantly, if this is what it takes to rigorously test the hypothesis—“20,000 to 30,000 participants needed to achieve satisfactory statistical power”— then why are we even discussing these other trials with nothing like that number? (And, of course, that’s why they had to dismiss the WHI as meaningful because that trial does have this kind of statistical power.) They’ve effectively eviscerated their own case. If this was a legal case, the judge would now throw it out and we’d all be having coffee in the lobby (with or without cream) discussing how this fiasco played out and why it ever got to court to begin with. And this leads to the third point.

3. Did I say that the first rule of medicine, as Hippocrates pointed out, is do no harm? I believe I did. Back in 1981, Geoffrey Rose, a pioneer thinker in the field of preventive medicine, wrote an article in the BMJ on the strategy of preventive medicine, and he pointed out the same problem about vegetable oils that confronts us today. Again history keeps repeating itself in this world, in part because these researchers and authorities don’t think we have to do the experiments necessary to resolve this controversy and find out if the AHA’s hypothesis is indeed true. They’re too hard. (Imagine if physicists took this tack with their science. Why bother raising ten billion dollars to build a single accelerator so technology challenging that we have to work out the technological details as we go along, just because that’s what’s necessary to answer the next question they want to see answered? Too hard. They’ll never do it. Let’s not try. We can speculate and pretend it’s fact. Sigh.) As Rose observed, it’s one thing to tell people not to eat something because we evolved to eat very little of it and there’s good evidence that eating less of it will reduce chronic disease risk. This is what Rose called removing an “unnatural factor and the restoration of `biological normality’—that is, of the conditions to which presumably we are genetically adapted.” As Rose put it, “Such normalizing measures [for instance, telling people not to smoke] may be presumed to be safe, and therefore we should be prepared to advocate them on the basis of a reasonable presumption of benefit.”

But telling people to eat something new to the environment — an unnatural factor, à la virtually any vegetable oil (other than olive oil if your ancestor happen to come from the Mediterranean or mid-East), which was what concerned Rose and concerns us today — is an entirely different proposition. Now you’re assuming that this unnatural factor is protective, just like we assume a drug can be protective say by lowering our blood pressure or cholesterol. And so the situation is little different than it would be if these AHA authorities were concluding that we should all take statins prophylactically or beta blockers. The point is that no one would ever accept such a proposal for a drug without large-scale clinical trials demonstrating that the benefits far outweigh the risks. So even if the AHA hypothesis is as reasonable and compelling as the AHA authors clearly believe it is, it has to be tested. They are literally saying (not figuratively, literally) that vegetable oils — soy, canola, etc — are as beneficial as statins and so we should all consume them. Maybe so, but before we do (or at least before I do), they have a moral and ethical obligation to rigorously test that hypothesis, just as they would if they were advising us all to take a drug. And then, well, they should probably do it twice, since a fundamental tenet of good science is also independent replication. And what we need here is good science.

]]>The Case Against Sugar debuted on December 27, 2016 to much praise, from reviewers in publications as wide-ranging as The Wall Street Journal to The New York Times to The Economist, The Atlantic to Men’s Journal and more. A sampling, with links to the full stories:

“This is required reading for not only every parent, but every American.”
—Katie Couric

“Taubes’s argument is so persuasive that, after reading The Case Against Sugar, this functioning chocoholic cut out the Snacking Bark and stopped eating cakes and white bread…The Case Against Sugar should be a powerful weapon against future misinformation.”—Eugenia Bone, The Wall Street Journal

“No one in this country has worked harder on or better understood the role of sugar in our diet than Gary Taubes. As a journalist, an investigator, a scientist, and an advocate, he is without peer. (Plus, he knows how to write.) The Case Against Sugar is not only a terrific history but a forward-thinking document that can help us think more intelligently about how (and how not) to eat.” —Mark Bittman, author of How to Cook Everything Fast

“Taubes’s prose is perfectly judged, as compelling as a slow-motion car crash… A book to make you put your head in your hands and wonder how humanity has endured for so long… The sugar industry […] has survived a century-long persecution and survived it well… Can Taubes’s expertly written and disturbing book change [this] culture?… full marks–and a big red lollipop–for making a valiant and vital attempt.”—Michael Brooks, New Statesman

“Compelling… Perhaps at long last, sugar is getting its just desserts.”
—The Economist

“I can’t think of another journalist who has had quite as profound an influence on the conversation about nutrition.”
—Michael Pollan, author of The Omnivore’s Dilemma and In Defense of Food

“Practically everything one wants to know about sugar—its history, its geography, the addiction it causes—is here. In the end, each of us is confronted with a choice. Continue consuming sugar at our current level and suffer the ill effects. Or reduce, if not eliminate, it from our diet, thereby improving our odds of living a long, healthy life.” —The Seattle Times

“A riveting history of ideas, a clear analysis of evidence, and an utterly persuasive argument that sugar is the new tobacco. Taubes methodically explains why sugar—not sloth, not fat—accounts for our unprecedented levels of obesity, cancer, diabetes, and heart disease. Taubes answers every counter-argument as he exposes bad research, reveals conflicts of interest, and explodes myths.” —Gretchen Rubin, author of The Happiness Project

“A courageous and meticulous documentation of the health dangers of sugar. No one has hit the political and economic forces behind this ‘acceptable’ addiction as clearly and unflinchingly. The information in this book will, quite literally, save your life if you apply it.” —Christiane Northrup, M.D., author of Women’s Bodies, Women’s Wisdom

“If you ever doubted that sugar is the root cause of our obesity, diabetes, and heart disease epidemic, then look no further than The Case Against Sugar. This deeply researched, well-reasoned exploration of the history and biology of sugar would convince any supreme court of nutrition that it is sugar, not fat, that should be indicted and limited. Doctors, scientists, policymakers, and concerned eaters would do well to heed Gary Taubes’s advice.” –Mark Hyman, M.D., author of The Blood Sugar Solution

]]>Thought experiments are devices of the imagination used to investigate the nature of things…. The primary philosophical challenge of thought experiments is simple: How can we learn about reality (if we can at all), just by thinking? More precisely, are there thought experiments that enable us to acquire new knowledge about the intended realm of investigation without new data?… [T]hought experiments can disclose nature’s failure to conform to a previously held set of expectations. In addition, they can suggest particular ways in which both expectation and theory must henceforth be revised.

When we announced the creation of the Nutrition Science Initiative (NuSI) in September, I e-mailed information packets and press releases to many of my colleagues in science and health journalism. I included a few who had vehemently and even publicly disagreed with the arguments I’ve made in my books and articles. I was hoping to see them embrace the idea that our conflicting ideas should and could be tested, and any organization that could make this happen would be a good thing and worthy of public support. They all expressed their admiration for the effort in private, although none of them perceived it as worth writing up publicly, at least not until we have experimental results to discuss.

A couple of these journalists took the opportunity to insist that we didn’t really disagree all that much on what we had argued over the years, and they explained why. Here’s how one put it in an e-mail back to me (and I’ve made a few minor changes so that this writer can remain suitably anonymous):

I think in our hearts we basically agree–I know you believe in calories because you are a scientist. And I know you know that many cultures that consume large amounts of carbohydrate but low fat and relatively low sugar are not fat. (Japan, for example–and yes, I know they are not all slim, but we both know they have a very low obesity rate.) Do I think some people get “hooked” on sweets and simple carbs? Yes, I do. Do I think that can be a problem for some people, hence steering clear of simple carbs is a good idea for them? I do, and said so on national radio. Do I think there is strong evidence that carbs per se (in absence of excess calories) will result in excess weight gain? No I do not.

As I wrote in […]–sugar ain’t great, nor is excess fat. Sugar and fat infused foods packaged and “ready to eat” are what has made America obese–sitting around doesn’t help, either. When you cut out carbs, you cut out most of the fat–yes, you can eat pure whipped cream, but not the cake or ice cream you’d normally put it on–so why bother? Yes, you can eat fried porkchops, but not breaded fried porkchops–or clams or chicken–so that cuts calories a bunch. You eat a lot less butter when you can’t eat the bread or potato or pasta or cake or pie…etc, etc. There is only so much “whole chicken and steak” and even full fat yogurt with berries that most of us can stomach. So over time, on average we eat fewer calories when we cut out simple and most complex carbs.

I thought a lot about this e-mail after I received it, for two reasons. One is the tendency we all have (or at least I have and I do see it in others) to assume that just because we’ve written something, other people have read it or certainly should have read it. If only…

In this case, I addressed the no-bread-no-butter, only-so-much-steak-you-can-eat position at length in Good Calories, Bad Calories (The Diet Delusion, in the UK). I even quoted Jane Brody making this point in the epigraph to Chapter 20 — “Unconventional Diets” — and set it off against a quote from the DuPont physician Alfred Pennington to contrast how the same observation — weight loss and absence of hunger on a diet unrestricted in calories — could be perceived by one person, Pennington, as a “mighty stimulant to thought on the matter,” and by another, Brody, as a triviality to be dismissed without much concious thought (as my friend did in the e-mail).

Here’s Pennington in 1954:

Here was a treatment, that, in its encouragement to eat plentifully, to the full satisfaction of the appetite, seemed to oppose not only the prevailing theory of obesity but, in addition, principles basic to the biological sciences and other sciences as well. It produced a sense of puzzlement that was a mighty stimulant to thought on the matter.

And here’s Brody nearly half a century later:

Does it help people lose weight? Of course it does. If you cannot eat bread, bagels, cake, cookies, ice cream, candy, crackers, muffins, sugary soft drinks, pasta, rice, most fruits and many vegetables, you will almost certainly consume fewer calories. Any diet will result in weight loss if it eliminates calories that previously were overconsumed.

So rather than get upset at my journalist friend who either hadn’t bothered to read GC,BC after all these years, or read it and found it thoroughly forgettable, I got to thinking about the other point made in the e-mail: “I know you believe in calories because you are a scientist.”

Ignoring the possible mischaracterization of me as a scientist, this statement is a little ambiguous. I obviously believe in calories as a measure of energy, whatever that means to believe in such a thing. (It’s like believing in miles as a measure of distance.) So that’s probably not what my friend meant. What I don’t believe in is that discussions of caloric consumption and expenditure tell us anything meaningful about why we get fat or why we lose fat, and I believe that the mantra that ‘a calorie is a calorie is a calorie” serves only to direct attention away from the meaningful characteristics of the macronutrients in our diets.

I’ve been arguing that the original sin in obesity research is this belief that our body fat is regulated by the amount of energy we consume and expend. I think this is simply the wrong way to think about obesity and the chronic diseases with which it associates, and it’s because this is the fundamental assumption underlying most obesity research, it’s the reason why we’ve made so little progress. (And to those who think we have made real progress, I suggest they take a look around at the people walking by and reconsider.)

Another way to put this is that I think this energy balance hypothesis of obesity is an incorrect paradigm and it has to be replaced with a correct paradigm before progress will be made. Obviously my friend doesn’t understand this, so it got me thinking about yet other ways to explain it that might get the point across. This led me to a series of thought experiments, or gedankenexperiments as I learned to call them back in the days when I was writing about physics rather than nutrition and health.

The great thing about thought experiments is that they come unfettered by financial, ethical or real world constraints. We can do virtually anything in these experiments and see what we think is likely to happen. And we can do it quickly. So here goes.

Let’s imagine we have a pair of identical twins, unimaginatively named A and B. They’re males, say, and 20-years-old. They’ve stopped growing and both are weight stable when this experiment begins. (Remember we can do anything we like in a thought experiment, so if we say they’re weight stable initially, then they are.) Now we measure their daily energy expenditure. Let’s say they both expend 3000 calories a day. Every day, day in and day out — 3000 calories. Again, this is true of both of them because they’re identical in all respects.

Now the experiment: We’re going to house A and B in our splendidly livable metabolic ward and keep them there for the next 20 years. (Thought experiments also come free of Institutional Review Boards. We don’t have to worry about whether this is ethical or not. Our imaginary twins will be perfectly happy anyway because we say so.) We’re going to feed them almost identical diets. Each one is going to get exactly 3000 calories a day so that their intake matches their initial expenditure. If we believe in calories, as my friend might have put it, the fact that we’re matching intake to expenditure and both twins are getting the same intake suggests they will both maintain a stable weight for the duration of the experiment.

But here’s the experimental twist: the diets are not identical, they’re only almost identical. They differ in the macronutrient content of ten percent of the calories. So 2700 calories of the two diets are identical. The other 300 calories of A’s diet will come from sugar — sucrose, to be precise, molecules of glucose bonded to molecules of fructose. In B’s diet, these 300 calories will come from glucose alone. So A will get 150 calories of fructose that B won’t get, and B will get 150 calories more glucose than A. Other than that the diets are indeed identical with all the macro and micronutrients necessary for the twins to flourish.

Now we run the experiment for 20 years. What happens? Care to guess? Will A and B still be identical after 20 years of A eating 300 calories of sugar every day that B does not eat?

We know sugar is metabolized differently from the glucose in starch because of the fructose component. Glucose is metabolized by cells throughout the body; fructose is metabolized primarily in the liver. We know the liver will turn some of this fructose into fat and if the fructose is delivered quickly enough (say in liquid form as sugar water), it likely to cause insulin resistance in the liver, which in turn might cause systemic insulin resistance. The extra 150 calories of glucose in B’s diet will stimulate more insulin secretion, although for B this will come in the absence of any fructose-induced effects in the liver. One way or the other, A and B will experience different metabolic and hormonal effects, despite eating precisely the same amount of calories in diets that are otherwise 90 percent identical. Their fat cells, for instance, will be on the receiving end of different hormonal and metabolic signals. As Claude Bernard would say, the fat cells would be living in a different milieu intérieur and this will effect how they change over time.

Another way of asking what happens in this experiment is to ask whether this difference in hormonal and metabolic responses to diets of equal caloric content will have a meaningful effect on, say, fat accumulation and risk factors of chronic disease. Indeed, why would we expect our twins to end up with identical body compositions, weights, and chronic diseases risk factors, when their hormonal and metabolic experiences over those 20 years are indeed different? The question, of course, is are these differences meaningful.

If we’re wedded to the energy balance way of thinking — if we believe in calories, as my friend said — we’re supposed to predict that the twins will end up identical. (That’s certainly what the sugar industry would like us to think. Although the industry might even argue, based on observational studies from the 1980s, that the twin eating sugar will end up leaner and healthier than the twin getting only glucose.)But we’re also likely to maybe hedge a little bit. Okay, maybe the twins will have slightly different body compositions after 20 years. Maybe they’ll even have slightly different chronic disease risks, depending on how this sugar-starch/fructose-glucose trade-off plays out. Surely, though, they’ll weigh the same. After all, they’re consuming identical calories and these calories are exactly matched to their initial expenditure. So they should end up weighing what they weighed at the get-go and they should both weigh the same. Yes? (And by the way, this thought experiment also negates any effects of food reward or the addictive nature of sugars, because we’re limiting consumption and so even if sugar is addictive and A wants to eat more of it, he’s not getting the opportunity.)

But the fact is even their weight’s can differ, because we’ve only fixed caloric intake. We haven’t fixed their expenditure. Both will get exactly the same number of calories. That’s a condition of the thought experiment. But the different macronutrient composition of A’s diet vs. B’s, can have an effect on fat accumulation and so weight despite caloric consumption being equivalent.

Here’s how: let’s assume two things for the sake of argument. First, the sugar in A’s diet causes A to become insulin resistant. And second, insulin works to put fat in fat tissue. There’s some evidence for our first assumption and the second assumption is in the textbooks; there’s a lot of evidence for that.

Now as A becomes insulin resistant, his pancreas has to secrete more insulin than B’s to handle the equivalent carbohydrate load. So A now might have higher circulating levels of insulin than B. If he does, this means more calories might be fixed in A’s fat tissue than in B’s. Put simply, A might now be getting fatter than B. And as A gets fatter, his body has to compensate for the calories that are being locked away in the fat tissue and for the greater metabolic demands of a heavier body. What does A do?

What A can’t do is eat more, because we’ve fixed his caloric consumption at 3000 calories per day. One option is he could cannibalize his lean mass to feed his growing fat tissue. This can certainly be done without violating any thermodynamic laws. Now A gets fatter while simultaneously losing muscle mass and his weight remains more or less the same. A second option is that A’s body merely expends less energy to make up for the calories being locked away in fat tissue and the greater caloric requirement that comes from being heavier.

Now A gets fatter while his energy expenditure goes down. While B remains in energy balance throughout the experiment — eating 3000 calories a day to match the 3000 calories he expends — A moves into positive energy balance. He’s still consuming 3000 calories every day, but he’s expending less. And the reason he’s in positive energy balance is because he’s amassing fat in his fat tissue and getting heavier. (Although a naive observer, wedded to the energy balance, might decide that A has become a couch potato and that’s why he’s getting fatter. In this case, the direct effect of the sugar is to make A expend less energy and this in turn causes the energy imbalance that makes A fat. The causality is reversed.)

So here’s a possible chain of events in our thought experiment that’s perfectly consistent with the laws of thermodynamics but is inconsistent with the notion that a calorie is a calorie is a calorie: first, the sugar causes A to be insulin resistant; second, the insulin resistance serves to cause a compensatory elevation of serum insulin levels; third, the elevated insulin causes A to store calories in his fat tissue every day and grow fatter.

If this effect is tiny, say, five calories worth of fat get trapped in A’s fat cells every day, he’ll still put on ten pounds of fat over the 20 years of the experiment and weigh 10 pounds more than his genetically-identical brother eating his almost identical diet. If this fat-trapping amounts to 20 calories a day — still less than one percent of the calories A is consuming — that would amount to forty pounds of excess fat over the course of the experiment. It would still be too subtle of an effect to be observable in the relatively short-term experiments done to date on sucrose consumption.

Now, assuming this did happen, or at least could happen, it would lead us to some other interesting observations as well. For instance, if A puts on this fat above the waist, it will increase his heart disease risk. The more fat he gains, the greater his risk of diabetes. In fact, depending on the size of the effect, he might become diabetic over the course of the study. His brother might not. A’s cancer risk goes up, as well, with his adiposity. So does his risk of getting Alzheimer’s. All without consuming a single calorie more than his twin brother did. In fact, if we run the experiment long enough, the brothers might die of different diseases and one might out live the other by a significant amount.

If you believe this scenario is a possibility, even a likely possibility, as I do, you still believe in the laws of thermodynamics. You’re still thinking like a scientist (as my friend might say). But now, I hope, you can see what I mean by calories being the wrong paradigm. If we believe in calories,as my friend put it, then we believe that the twins end up identical, just as they started, because the quantity of calories consumed in the two diets was identical and it’s quantity that matters, not quality. What do you believe?

We’ve started with identical twins, hence the very same genetic make-up. We’ve fed them diets of identical calories. We’ve made a relatively subtle change in macronutrient composition. Do we end up with twins that are still identical; or do we end up with one twin fatter and perhaps sicker than the other? And, keep in mind, as I said, that both twins are limited to 3000 calories a day, and we’re making them eat all 3000, so any addictive effects of the sugar, say, are not relevant. (And if food reward characteristics are meaningful, they have to manifest themselves via the periphery — increasing fat mass, for instance, through central nervous system stimulation of adipocytes — not merely by making us want to eat more. )

Now we can do a host of variations on this experiment. For instance, we can start off with two villages — A and B. Each village has one of each pair of 5000 identical twins. So each sibling pair is identical, but the 5000 sets of twins are as genetically diverse as any 5000 individuals chosen at random. We put 5000 siblings in village A and their 5000 twins in village B. Now we do the same experiment on this population scale. We measure their energy expenditures. We match intake to expenditure for each pair of twins. Then the twins in village A all get ten percent of their calories as a sucrose-sweetened beverage. The twins in village B get ten percent of theirs as a beverage with glucose, not the glucose-fructose mixture that village A is getting.

Now let’s run it out for 20 years. Do villages A and B end up with exactly the same number of obese villagers, exactly the same incidence of diabetes? Heart disease? Cancer? If we run it out for decades, do the two villages have the same mortality rates? The only difference in their diets is the type of carbohydrate that’s sweetening their daily drinks. (And remember, this is a thought experiment: each villager is eating and drinking precisely what we say they’re eating and drinking because they’re under our imaginary control. No propositions need be voted on. We get perfect compliance to our interventions.)

If you believe in the primacy of calories, or you’re a sugar industry spokesperson, then you believe that the two villages start off identical and they end up identical. (Or, for the sugar industry spokespeople, maybe Village A ends up healthier.) If you believe that one village is going to end up fatter and sicker than the other because they’re experiencing different metabolic and hormonal experiences for 20 years, then you’re thinking as I now think and Robert Lustig has argued so publicly. It’s not about the calories; it’s about what those macronutrients do metabolically and hormonally. And who knows what else, maybe the sucrose has an effect on gut biota that the glucose alone does not,or vice versa, and if the two twins develop different bacterial populations in their guts, then this might induce a whole host of other downstream differences that could effect their weight and health.

We can play these thought experiments all day long. That’s the joy of gedankenexperiments. They’re ridiculously inexpensive and we can do them fast. Twenty years in a gedankenexperiment can be instantaneous in reality.

How about this one: instead of feeding twin B (or village B) glucose instead of sucrose, what if he (or it) got dietary fat. So now twin A gets ten percent of his calories as sugar water — pick your poison, so to speak, soda or fruit juice. Now we’re accelerating the delivery method in this thought experiment by making sure these calories are digested quickly. Twin B gets liquid fat, say heavy cream. watered down so that the energy density is effectively identical to the sugar water. So we control for energy density, a factor that the authorities think is key to weight gain. But we dramatically change the macronutrient content of these 300 calories — glucose and fructose calories for A, fat calories for B. Now the hormonal and metabolic responses to these 300 calories are entirely different. Nothing subtle about it. What happens over 20 years? Same body composition, same disease status because the calories are identical? Yes or no?

What if we play more extreme variations with the diets. Rather than play with just ten percent of the calories they consume, let’s play with 50 percent of them. Twin A (or village A) gets his (or its) calories as a standard American diet, replete with 50 percent carbohydrates, of which, say, a fourth is sugar or high fructose corn syrup as is about the case today in the standard American diet. Twin B (or village B) gets a paleo diet or even a ketogenic diet, same amount of calories, far fewer to almost no carbohydrates, far more fat. What happens? Both twins (or villages) eat precisely the same amount of calories (each or per capita) every day for 20 years. Do they end up identical. Is village A healthier and leaner or village B or neither?

Now let’s change it up entirely, and this will be the last experiment I’ll suggest for the moment. Rather than start with genetically identical twins eating different diets and so generating different hormonal/metabolic responses that way, let’s start with subjects who are not genetically identical, and give them the identical diet. So we can use fraternal twins or siblings, or total strangers as our subjects, but now feed them the exact same diet. We’ll choose our subjects so that they’re the same age, to the day, the same height and weight and they expend the exact same amount of energy every day (at least when the experiment begins). They’re both equally healthy. And now we feed them the same diet — intake matched perfectly to expenditure — with 10 percent of the calories coming as sucrose. What happens? How do they change over the twenty years of the experiment, given the exact same diet, precisely the same calories, precisely the same physical characteristics, but different genetic make-ups?

The differences in their DNA means they’ll almost assuredly have different hormonal and metabolic responses to the diet. Maybe one does a slightly better job of metabolizing fructose in the liver than the other does. Maybe one secretes a little more insulin in response to the glucose, or is a little more sensitive or resistant to the insulin secreted. Maybe the gut biota in one responds differently. Maybe leptin resistance develops in one but not the other. Anything can happen right, because genes ultimately determine all these responses and their genes are different.

So we’re feeding them exactly the same diet — same quality and quantity — but the hormonal and metabolic responses are going to be different. Their milieu intérieur is going to be different. Maybe a little different as the years go by; maybe a lot. We don’t know. They may start out relatively identical in relevant physical characteristics, but little by little, they’re going to diverge. Why would we expect them to end up with with the same weight, same fat mass, and even the same chronic disease risk profile?

And if all these things do end up different, would our belief in calories have led us to the same understanding of what happened and why?

If we could do this experiment in real life, it wouldn’t really matter what we believe. Right? We just do the experiment and see how it comes out. (And this is what NuSI hopes to achieve, albeit in far more realistic experiments.) Because we can’t do the experiments, we can do these thought experiments instead and inform our understanding. Time permitting, more will be coming in later posts.

One last note before I conclude here. Let’s go back to our original experiment with twins A and B and their almost identical diets. Imagine, now, as I suggested, that A gets fatter than B and even heavier, because of the effect of the sugar in A’s diet on hepatic metabolism and insulin sensitivity and so serum insulin levels and fat accumulation in fat cells (and maybe all those other factors like gut biota). But A is never able to eat more to compensate for this loss of calories into his fat tissue and his increasing weight, because we don’t let him: we’ve fixed his caloric intake. As a result it’s safe to assume that A would be hungrier than B is for the entire 20 years. B can eat to satisfy the metabolic requirements of his body; A cannot. How would that manifest itself? Would A at least feel like binging on occasions? Could we create a binge eating disorder that never gets to manifest itself in this particular thought experiment, just by changing the macronutrient composition of the diet?

You can see how thought experiments can lead us to all kinds of conclusions and (at least hypothetical) observations that might not be intuitively obvious otherwise.

I could go on. I’m hoping the point is clear.

Getting back to my friend’s e-mail: Yes, I believe that calories are a useful measure of the energy contained in the foods we consume and a useful measure of the energy our bodies expend. (Just as I believe miles are a useful measure of how far I have to travel to get, say, from Oakland to Los Angeles.) Yes, I believe in the laws of thermodynamics and I believe, as I say in both my books, they always hold true. That’s why we call them laws. But, no, I do not believe that we can learn anything useful about why people get fat or why they get the diseases that associate with getting fat, by focusing on the calories they consume and expend. It’s not about the calories.

This morning, at 9am Eastern Time, we officially launched The Nutrition Science Initiative — NuSI (pronounced “new see”). NuSI is a non-profit organization, technically a 501(c)(3). Its purpose is to facilitate and fund rigorous, well-controlled experiments targeted at resolving unambiguously many of the outstanding nutrition controversies — to answer the question definitively of what constitutes a healthy diet.

Our conventional dietary wisdom, as I’ve described in my books, is based on science that was simply not adequate to the task of establishing reliable knowledge — poorly-controlled human experiments, observational studies incapable of establishing cause and effect, and animal studies that may or may not say anything meaningful about what happens in humans. NuSI was founded to address this issue and by doing so, we hope, reduce the social and economic burden of obesity and its related diseases. NuSi’s co-founder, and my collaborator in this endeavor, is Peter Attia, who will serve as NuSI’s president.

Peter and I started NuSI as a nights and weekends endeavor with the hope of raising the necessary money to keep the organization running and fund the necessary experiments using crowdfunding techniques on the internet. Last November, however, we heard from the Laura and John Arnold Foundation expressing interest in what we were trying to accomplish. After many meetings and their due diligence, LJAF has provided NuSI with a seed grant to get our organization up and running and a verbal commitment to help fund some of the key studies. For the past six months, we’ve been working days, nights, and weekends to make it happen. We’ve opened an office in San Diego (where Peter lives) and, as mentioned in my previous post, we’re hiring staff, a research associates and eventually a research director as well.

As to the mission itself, Peter and I have already had meetings with researchers from around the country to discuss and begin the design process of the research that NuSI hopes to fund. These researchers are all excellent scientists, and they’re all skeptical of the hypotheses that we hope to test — the ideal combination. The experiments will be human trials; they’ll all be rigorously well-controlled, and they’ll all be aimed at identifying unambiguously the causes of obesity and type 2 diabetes, elucidating the underlying mechanisms involved. If all goes well, we’ll move later onto studies that look at longterm effectiveness of dietary therapies based on what we’ve learned.

Both Peter and I have our beliefs about what we’re likely to find, as do the researchers we’ve recruited to join the effort. As we say in our founder’s letter on the NuSI site, we’re not invested in particular outcomes, we’re invested in establishing reliable knowledge on the relationship between diet and disease and so scientifically-sound solutions to the health problems that beset us. One of the quotes that we use on the NuSI website and that I’ve taken to using in my lectures is particularly apt. It’s from Robert Burton’s 1893 book, The Anatomy of Melancholy: “It is in vain to speak of cures, or think of remedies, until such time as we have considered of the causes . . . cures must be imperfect, lame, and to no purpose, wherein the causes have not first been searched.”

NuSI was founded on the premise that the reason we are beset today by epidemics of obesity and type 2 diabetes, and the reason physicians and researchers think these diseases are so recalcitrant to dietary therapies, is because of our flawed understanding of their causes. We believe that with a concerted effort and the best possible science, this problem can be fixed. We hope you’ll give your support to NuSi in anyway you can.

I promised in my last post — yes, far too long ago — that I would give an update on the Metabolism, Diet and Disease Conference, which was held at the end of May in Washington, DC. As the months passed, I was waiting to hear from the organizers that they had posted a video of the panel discussion that ended the conference, and now, as of a few days ago, they have.

The conference itself was rather remarkable. The idea was to bring together from all disciplines researchers working on the various pathologies associated with insulin resistance. It was organized by the editors of BioMed Central, who had come upon the idea after reading The Diet Delusion, which is the British edition of Good Calories, Bad Calories. I was enlisted to help organize and suggest and recruit speakers and executive committee members. The conference also provided the opportunity to get researchers who had worked on carb-restricted diets — Eric Westman and Jeff Volek, in particular — presenting in a non-nutrition venue to researchers who might otherwise never take their work seriously or at least never imagine that it had relevance to their research in insulin resistance, hyperinsulinemia and the related pathologies. Eugene Fine was also there with a poster on his just published pilot study on ketogenic diets and cancer — “Targeting insulin inhibition as a metabolic therapy in advanced cancer: A pilot safety and feasibility dietary trial in 10 patients.”

What I found most fascinating about the conference was how beliefs shifted over the course of the three day event, from unconditional faith in the conventional wisdom to openness and scientific curiosity about the kinds of alternative hypotheses put forward by myself and others. On the first day of the conference I was having arguments/discussions with researchers about the laws of thermodynamics and how they apply to obesity (or don’t, as I believe) only to find myself sitting with them on a panel on day three as they agreed that the role of refined grains and sugars in cancer and cancer therapy had to be taken seriously.

With that, I highly recommend reading the BioMed Central blog post on the last day’s panel discussion and then watching the video of the discussion itself to see how it played out. You can see for yourself how beliefs and opinions had shifted so that the outcome of the panel discussion was probably something that few of the researchers going in would have ever imagined. I’m not optimistic enough to think that this is a long term change in thinking, or at least not without other factors, experiments and influential researchers keeping the momentum up — and, of course, the science has to turn out to be right or at least mostly right. But it certainly gave me hope that the kinds of issues we’ve been raising again and again outside the research community will soon be addressed critically (i.e., not in a knee-jerk, dismissive manner) by researchers within the community.

This brings up item number two in this post, and here I’m going to be cribbing considerably from what Peter Attia recently posted on his blog — eatingacadmy.com . This is our update on NuSI, the Nutrition Science Initiative, and a job we’re hoping to fill in the near future.

As we’ve both alluded to in previous posts, Peter and I founded NuSI earlier this year. Peter is the president and I’m, well, the co-founder. (We rejected “provocateur-in-residence” on the basis that it only captured part of what I do and didn’t quite work officially for an organization that we, and the foundation supporting us, and the scientists with whom we’re working, all take very, very seriously.) NuSi is a non-profit organization with the mission of reducing the economic and social cost of obesity and its related chronic diseases. We hope to achieve this by facilitating and funding the kind of rigorous, meticulously well-controlled and targeted experimental research that has been conspicuously lacking in nutrition research for the half past century.

We’ll say much more about this when we formally and publicly launch NuSI in early September. The ultimate goal is to create what would ideally become a kind of Manhattan Project of Nutrition: a concerted, directed, well-funded research effort composed of the best scientists in the field — all independent and suitably skeptical — working together to generate the evidence necessary to put to rest, one way or the other, all the major and many of the minor controversies in nutrition research. Peter and I have already enlisted some of the researchers we’d like to get involved, and we’ve spoken to others about possible experiments that might be done in the future. Our hope is that regardless of any initial biases, the evidence generated in these experiments (and replicated in further experiments) will be suitably unambiguous that in, say, 15 years we’ll have little left to argue about. And if the evidence still leaves room for argument and controversy, then we’ll do more experiments until it doesn’t.

The best part, as Peter has pointed out, is that all this should be doable for less than the cost of developing just one drug in the United States.

Peter and I have been working obsessively to build a world-class team at NuSI, including our Board of Directors, Scientific Advisory Board, Board of Advisors, scientific consortium, and full-time staff. We can’t wait until we can formally introduce you to our team and collaborators.

We have already hired several positions within NuSI through standard recruiting channels and referrals, but there is one position, in particular, Peter and I thought would be worth bringing directly to the attention of our readers – our Research Associate. We’ve already received a few dozen tremendous applications from individuals with great credentials, but we’re wondering if one critical attribute may be missing or under-represented in our applicants so far. Beyond the tangible skills necessary for this particular role – outlined in the downloadable job posting (below) – this role requires an almost maniacal obsession with nutrition science and a passion for answering the kinds of questions we’ve all been debating in print and in our blogs. We think there’s a reasonable chance that our future Research Associate is one of you out there reading this right now.

For the full list of job responsibilities and requirements, please download the job posting, which also explains exactly how to apply. Please do not send any of the application materials to me or Peter directly. You can consider this the first test of the ability to follow simple instructions.

This position should prove to be both extremely challenging and highly rewarding. We think that we have the opportunity with this organization to change the world, and that the odds are pretty good that we can pull it off. Such opportunities don’t come along frequently in life. And as I said, we’ve already enlisted some of the best scientists in nutrition and obesity research to design and conduct the studies we’ll be funding; you’ll get the opportunity to support them day in and day out.

One very important disclosure: This role will make the proverbial “drinking from a fire hose” seem manageable. We consider this role (as we do our own) more of a calling than a job. If you’re interested, if you feel you meet the necessary requirements, and if we haven’t scared you off yet, please consider applying for this position.

Thank you, and we’re looking forward to sharing the progress of NuSI with all of you.

There’s an interesting conference starting Tuesday the 29th in Washington – today –and I should have written about it months ago. It’s the Metabolism, Diet and Disease conference being held at the Georgetown University Conference Center. The editors of BioMed Central, a British open-access science publishing company, are the organizers. They contacted me in October 2010 to tell me they had read the British version of Good Calories, Bad Calories – The Diet Delusion – and found it compelling. They were particularly struck by the notion that there are many disciplines involved in the science of obesity, diabetes and their associated chronic diseases, but they don’t read the same journals and they don’t tend to interact in conferences. So their idea was to put together a conference that would solve this problem. Between us, we recruited a first-rate executive board – including the two Nobel Laureates, Michael Brown and Joseph Goldstein – and put together a conference that, as I see it, includes most of the major issues revolving around and feeding into insulin resistance and metabolic syndrome.

The original idea – if you’ll pardon the cliché – is that insulin resistance and metabolic syndrome are the elephant, and we would get all these people in one room, who were studying the legs, the trunks, the tail, the ears, etc., and may not have realized quite what the whole elephant itself looked like. As the recruiting of speakers started, the conference evolved and took on a life of its own. Much of the original idea still exists, but there’s a fair share of the latest esoteric research ideas, for good or for bad (sirtuins, irisin, etc.), which may or may not have anything to do with the elephant itself. But we still have some of the leading researchers in the world talking about everything from the epidemiology of insulin resistance to the pathologies that associate with it – diabetes, heart disease, cancer, aging, etc. – and the pathways and mechanisms that link them all together.

One session, which I’m chairing on Thursday morning, is called “Dietary factors in metabolic diseases,” and the speakers are all addressing the carbohydrate issue. We have Luc Tappy, the leading fructose biochemist in the world, talking about the role of fructose in metabolic disorders, and then Jeff Volek and Eric Westman talking about the effects of carb-restricted diets, suggesting that the carbohydrates (refined grains, of course, and sugars in particular) might be the fundamental problem. It will be interesting to see how the mainstream researchers take this, as they’re used to thinking about carb-restriction as quackery and now it will be presented as potentially mainstream itself.

On Saturday, I’m in Scottsdale Arizona talking at the National Lipid Associations annual convention. This, too, should be interesting, as I’ll be presenting my Why We Get Fat lecture an hour after Robert Eckel speaks. Eckel is a former president of the American Heart Association who is on record saying that he doesn’t even think low-carb-high-fat diets should ever be tested, that it’s unethical, because they’re so dangerous. After I speak, I’ll get to hear Rob Lustig and Peter Havel talk about sugar and fructose. As I said, it should be an interesting day and an interesting week.

I’m writing this post with a little more haste than is my wont. I’ve received dozens of e-mails asking me to comment on the recent news — ala the the New York Times — that meat-eating apparently causes premature death and disease. So this post is likely to contain more than my usual number of typos, egregious spelling mistakes, grammatical errors, etc. Bear with me. Rather than spend a week rewriting and editing, as I usually do, I’m going to do my best to get this up and out in a few hours.

Back in 2007 when I first published Good Calories, Bad Calories I also wrote a cover story in the New York Times Magazine on the problems with observational epidemiology. The article was called “Do We Really Know What Makes Us Healthy?” and I made the argument that even the better epidemiologists in the world consider this stuff closer to a pseudoscience than a real science. I used as a case study the researchers from the Harvard School of Public Health, led by Walter Willett, who runs the Nurses’ Health Study. In doing so, I wanted to point out one of the main reasons why nutritionists and public health authorities have gone off the rails in their advice about what constitutes a healthy diet. The article itself pointed out that every time in the past that these researchers had claimed that an association observed in their observational trials was a causal relationship, and that causal relationship had then been tested in experiment, the experiment had failed to confirm the causal interpretation — i.e., the folks from Harvard got it wrong. Not most times, but every time. No exception. Their batting average circa 2007, at least, was .000.

Now it’s these very same Harvard researchers — Walter Willett and his colleagues — who have authored this new article claiming that red meat and processed meat consumption is deadly; that eating it regularly raises our risk of dying prematurely and contracting a host of chronic diseases. Zoe Harcombe has done a wonderful job dissecting the paper at her site. I want to talk about the bigger picture (in a less concise way).

This is an issue about science itself and the quality of research done in nutrition. Those of you who have read Good Calories, Bad Calories (The Diet Delusion in the UK) know that in the epilogue I make a point to say that I never used the word scientist to describe the people doing nutrition and obesity research, except in very rare and specific cases. Simply put, I don’t believe these people do science as it needs to be done; it would not be recognized as science by scientists in any functioning discipline.

Science is ultimately about establishing cause and effect. It’s not about guessing. You come up with a hypothesis — force x causes observation y — and then you do your best to prove that it’s wrong. If you can’t, you tentatively accept the possibility that your hypothesis was right. Peter Medawar, the Nobel Laureate immunologist, described this proving-it’s-wrong step as the “the critical or rectifying episode in scientific reasoning.” Here’s Karl Popper saying the same thing: “The method of science is the method of bold conjectures and ingenious and severe attempts to refute them.” The bold conjectures, the hypotheses, making the observations that lead to your conjectures… that’s the easy part. The critical or rectifying episode, which is to say, the ingenious and severe attempts to refute your conjectures, is the hard part. Anyone can make a bold conjecture. (Here’s one: space aliens cause heart disease.) Making the observations and crafting them into a hypothesis is easy. Testing them ingeniously and severely to see if they’re right is the rest of the job — say 99 percent of the job of doing science, of being a scientist.

The problem with observational studies like those run by Willett and his colleagues is that they do none of this. That’s why it’s so frustrating. The hard part of science is left out and they skip straight to the endpoint, insisting that their interpretation of the association is the correct one and we should all change our diets accordingly.

In these observational studies, the epidemiologistsestablish a cohort of subjects to follow (tens of thousands of nurses and physicians, in this case) and then ask them about what they eat. The fact that they use questionnaires that are notoriously fallible is almost irrelevant here because the rest of the science is so flawed. Then they follow the subjects for decades — 28 years in this case. Now they have a database of diseases, deaths and foods consumed, and they can draw associations between what these people were eating and the diseases and deaths.

The end result is an association. In the latest report, eating a lot of red meat and processed meat is associated with premature death and increased risk of chronic disease. That’s what they observed in the cohorts — the observation. The subjects who ate the most meat (the top quintile) had a 20 percent greater risk of dying over the course of the study than the subjects who ate the least meat (the bottom quintile). This association then generates a hypothesis, which is why these associations used to be known as “hypothesis-generating data” (before Willett and his colleagues and others like them decided they got tired of their hypotheses being shot down by experiments and they’d skip this step). Because of the association that we’ve observed, so this thinking goes, we now hypothesize that eating red meat and particularly processed meat is bad for our health and we will live longer and prosper more if we don’t do it. We hypothesize that the cause of the association we’ve observed is that red and processed meat is unhealthy stuff.

Terrific. We have our bold conjecture. What should we do next?

Well, because this is supposed to be a science, we ask the question whether we can imagine other less newsworthy explanations for the association we’ve observed. What else might cause it? An association by itself contains no causal information. There are an infinite number of associations that are not causally related for every association that is, so the fact of the association itself doesn’t tell us much.

Moreover, this meat-eating association with disease is a tiny association. Tiny. It’s not the 20-fold increased risk of lung cancer that pack-a-day smokers have compared to non-smokers. It’s a 0.2-fold increased risk — 1/100th the size. So with lung cancer we could buy as a society the observation that cigarettes cause lung cancer because it was and remains virtually impossible to imagine what other factor could explain an association so huge and dramatic. Experiments didn’t need to be done to test the hypothesis because, well, the signal was just so big that the epidemiologists of the time could safely believe it was real. And then experiments were, in effect, done anyway. People quit smoking and lung cancer rates came down, or at least I assume they did. (If not, we’re in trouble here.) When I first wrote about the pseudoscience of epidemiology in Science back in 1995, “Epidemiology Faces It’s Limits”, I noted that very few epidemiologists would ever take seriously an association smaller than a 3- or 4-fold increase in risk. These Harvard people are discussing, and getting an extraordinary amount of media attention, over a 0.2-fold increased risk. (Horn-blowing alert: my Science article has since been cited by over 400 articles in the peer-reviewed medical literature, according to Thomson Reuter’s Web of Knowledge.)

So how can we explain this tiny association between the risk of eating a lot of red and processed meat — the 1/100th-the-size-of-the-lung-cancer-cigarette effect–compared to eating virtually none? Again, we have an observation — or an association, two or more things happening in concert; let’s think of all the possible reasons that might explain why these two variables, meat-eating and disease, associate together in our cohorts of nurses and physicians. Here’s how the great German pathologist Rudolph Virchow phrased this in 1849: How, he said, can we “with certainty decide which of two coexistent phenomena is the cause and which the effect, whether one of them is the cause at all instead of both being effects of a third cause, or even whether both are effects of two entirely unrelated causes?” This is the hard part.

The answer ultimately is that we do experiments, which is what Virchow went on to discuss. But we’ll get back to this in a minute. Before we get around to doing the experiments, we must rack our brains to figure out if there are other causal explanations for this association beside the the meat-eating one. Another way to think of this is that we’re looking for all the myriad possible ways our methodology and equipment might have fooled us. The first principle of good science, as Richard Feynman liked to say, is that you must not fool yourself and you’re the easiest person to fool. And so before we go public and commit ourselves to believing this association is meaningful and causal, let’s think of all the ways we might be fooled. Once we’ve thought up every possible, reasonable alternative hypotheses (space aliens are out on this account), we can then go about testing them to see which ones survive the tests: our preferred hypothesis (meat-eating causes disease, in this case) or one of the many others we’ve considered.

So let’s think of reasonable ways in which people who eat a lot of meat might be different from people who don’t, looking specifically for differences that might also explain some or all of the association we observed between meat-eating, disease and premature death. What else can explain this association, which might have nothing to do with whatever happens when we consume meat or processed meat?

Zoe Harcombe made this point beautifully using the Harvard data. The obvious clue is that as we move from the bottom quintile of meat-eaters (those who are effectively vegetarians) to the top quintile of meat-eaters we see an increase in virtually every accepted unhealthy behavior — smoking goes up, drinking goes up, sedentary behavior (or lack of physical activity) goes up — and we also see an increase in markers for unhealthy behaviors — BMI goes up, blood pressure, etc. So what could be happening here?

If you go back and read my New York Times Magazine article on this research, you’ll see that I discussed a whole host of effects, known technically as confounders — they confound the interpretation of the association — that could explain associations between two variables but have nothing to do biologically with the variables themselves. One of these confounders is called the compliance or adherer effect. Heres’ what I said about it in the article:

The Bias of Compliance

A still more subtle component of healthy-user bias has to be confronted. This is the compliance or adherer effect. Quite simply, people who comply with their doctors’ orders when given a prescription are different and healthier than people who don’t. This difference may be ultimately unquantifiable. The compliance effect is another plausible explanation for many of the beneficial associations that epidemiologists commonly report, which means this alone is a reason to wonder if much of what we hear about what constitutes a healthful diet and lifestyle is misconceived.

The lesson comes from an ambitious clinical trial called the Coronary Drug Project that set out in the 1970s to test whether any of five different drugs might prevent heart attacks. The subjects were some 8,500 middle-aged men with established heart problems. Two-thirds of them were randomly assigned to take one of the five drugs and the other third a placebo. Because one of the drugs, clofibrate, lowered cholesterol levels, the researchers had high hopes that it would ward off heart disease. But when the results were tabulated after five years, clofibrate showed no beneficial effect. The researchers then considered the possibility that clofibrate appeared to fail only because the subjects failed to faithfully take their prescriptions.

As it turned out, those men who said they took more than 80 percent of the pills prescribed fared substantially better than those who didn’t. Only 15 percent of these faithful “adherers” died, compared with almost 25 percent of what the project researchers called “poor adherers.” This might have been taken as reason to believe that clofibrate actually did cut heart-disease deaths almost by half, but then the researchers looked at those men who faithfully took their placebos. And those men, too, seemed to benefit from adhering closely to their prescription: only 15 percent of them died compared with 28 percent who were less conscientious. “So faithfully taking the placebo cuts the death rate by a factor of two,” says David Freedman, a professor of statistics at the University of California, Berkeley [who passed away, regrettably, in 2008]. “How can this be? Well, people who take their placebo regularly are just different than the others. The rest is a little speculative. Maybe they take better care of themselves in general. But this compliance effect is quite a big effect.”

The moral of the story, says Freedman, is that whenever epidemiologists compare people who faithfully engage in some activity with those who don’t — whether taking prescription pills or vitamins or exercising regularly or eating what they consider a healthful diet — the researchers need to account for this compliance effect or they will most likely infer the wrong answer. They’ll conclude that this behavior, whatever it is, prevents disease and saves lives, when all they’re really doing is comparing two different types of people who are, in effect, incomparable.

This phenomenon is a particularly compelling explanation for why the Nurses’ Health Study and other cohort studies saw a benefit of H.R.T. [hormone replacement therapy, one subject of the article] in current users of the drugs, but not necessarily in past users. By distinguishing among women who never used H.R.T., those who used it but then stopped and current users (who were the only ones for which a consistent benefit appeared), these observational studies may have inadvertently focused their attention specifically on, as Jerry Avorn says, the “Girl Scouts in the group, the compliant ongoing users, who are probably doing a lot of other preventive things as well.”

It’s this compliance effect that makes these observational studies the equivalent of conventional wisdom-confirmation machines. Our public health authorities were doling out pretty much the same dietary advice in the 1970s and 1980s, when these observational studies were starting up, as they are now. The conventional health-conscious wisdom of the era had it that we should eat less fat and saturated fat, and so less red meat, which also was supposed to cause colon cancer, less processed meat (those damn nitrates) and more fruits and vegetables and whole grains, etc. And so the people who are studied in the cohorts could be divided into two groups: those who complied with this advice — the Girl Scouts, as Avorn put it — and those who didn’t.

Now when we’re looking at the subjects who avoided red meat and processed meat and comparing them to the subjects who ate them in quantity, we can think of it as effectively comparing the Girl Scouts to the non-Girl Scouts, the compliers to the conventional wisdom to the non-compliers. And the compliance effect tells us right there that we should see an association — that the Girl Scouts should appear to be healthier. Significantly healthier. Actually they should be even healthier than Willet et al. are now reporting, which suggests that there’s something else working against them (not eating enough red meat?). In other words, the people who avoided red meat and processed meats were the ones who fundamentally cared about their health and had the energy (and maybe the health) to act on it. And the people who ate a lot of red meat and processed meat in the 1980s and 1990s were the ones who didn’t.

Here’s another way to look at it: let’s say we wanted to identify markers of people who were too poor or too ignorant to behave in a health conscious manner in the 1980s and 1990s or just didn’t, if you’ll pardon the scatological terminology, give a sh*t. Well, we might look at people who continued to eat a lot of bacon and red meat after Time magazine ran this cover image in 1984 — “Cholesterol, and now the bad news”. I’m going to use myself as an example here, realizing it’s always dangerous and I’m probably an extreme case. But I lived in LA in the 1990s where health conscious behavior was and is the norm, and I’d bet that I didn’t have more than half a dozen servings of bacon or more than two steaks a year through the 1990s. It was all skinless chicken breasts and fish and way too much pasta and cereal (oatmeal or some other non-fat grain) and thousands upon thousands of egg whites without the yolks. Because that’s what I thought was healthy.

So when we compare people who ate a lot of meat and processed meat in this period to those who were effectively vegetarians, we’re comparing people who are inherently incomparable. We’re comparing health conscious compliers to non-compliers; people who cared about their health and had the income and energy to do something about it and people who didn’t. And the compliers will almost always appear to be healthier in these cohorts because of the compliance effect if nothing else. No amount of “correcting” for BMI and blood pressure, smoking status, etc. can correct for this compliance effect, which is the product of all these health conscious behaviors that can’t be measured, or just haven’t been measured. And we know this because they’re even present in randomized controlled trials. When the Harvard people insist they can “correct” for this, or that it’s not a factor, they’re fooling themselves. And we know they’re fooling themselves because the experimental trials keep confirming that.

That was the message of my 2007 article. As one friend put it years ago to me (and I wish I could remember who so I could credit him or her properly), when these cohort studies compare mostly health advice compliers to non-compliers, they might as well be comparing Berkeley vegetarians who eat at Alice Water’s famous Chez Panisse restaurant once a week after their yoga practice to redneck truck drivers from West Virginia whose idea of a night on the town is chicken-fried steak (and potatoes and beer and who knows what else) at the local truck stop. The researchers can imply, as Willett and his colleagues do, that the most likely reason these people have different levels of morbidity and mortality is the amount of meat they eat; but that’s only because that’s what Willett and his colleagues have to believe to justify the decades of work and tens, if not hundreds, of millions of dollars that have been spent on these studies. Not because it’s the most likely explanation. It’s far more likely that the difference is caused by all the behaviors that associate with meat-eating or effective vegetarianism — whether you are, in effect, a Girl Scout or not.

This is why the best epidemiologists — the one’s I quote in the NYT Magazine article — think this nutritional epidemiology business is a pseudoscience at best. Observational studies like the Nurses’ Health Study can come up with the right hypothesis of causality about as often as a stopped clock gives you the right time. It’s bound to happen on occasion, but there’s no way to tell when that is without doing experiments to test all your competing hypotheses. And what makes this all so frustrating is that the Harvard people don’t see the need to look for alternative explanations of the data — for all the possible confounders — and to test them rigorously, which means they don’t actually see the need to do real science.

As I said, it’s a sad state of affairs.

Now we’re back to doing experiments — i.e., how we ultimately settle this difference of opinion. This is science. Do the experiments. We have alternative causal explanations for the tiny association between meat-eating and morbidity and mortality. One is that it’s the meat itself. The other is that it’s the behaviors that associate with meat-eating. So do an experiment to see which is right. How do we do it? Well you can do it with an N of 1. Switch your diet, see what happens. Or we can get more meaningful information by starting with your cohort of subjects and assigning them at random either to a diet rich red meat and processed meat, or to a diet that’s not — a mostly vegetarian diet. By assigning subjects at random to one of these two interventions, we mostly get rid of the behavioral (and socio-economic and educational…) factors that might associate with choosing of your own free will whether to be a vegetarian (or a mostly-vegetarian) or a meat-eater.

So we do a randomized-controlled trial. Take as many people as we can afford, randomize them into two groups — one that eats a lot of red meat and bacon, one that eats a lot of vegetables and whole grains and pulses-and very little red meat and bacon — and see what happens. These experiments have effectively been done. They’re the trials that compare Atkins-like diets to other more conventional weight loss diets — AHA Step 1 diets, Mediterranean diets, Zone diets, Ornish diets, etc. These conventional weight loss diets tend to restrict meat consumption to different extents because they restrict fat and/or saturated fat consumption and meat has a lot of fat and saturated fat in it. Ornish’s diet is the extreme example. And when these experiments have been done, the meat-rich, bacon-rich Atkins diet almost invariably comes out ahead, not just in weight loss but also in heart disease and diabetes risk factors. I discuss this in detail in chapter 18 of Why We Get Fat, “The Nature of a Healthy Diet.” The Stanford A TO Z Study is a good example of these experiments. Over the course of the experiment — two years in this case — the subjects randomized to the Atkins-like meat- and bacon-heavy diet were healthier. That’s what we want to know.

Now Willett and his colleagues at Harvard would challenge this by saying somewhere along the line, as we go from two years out to decades, this health benefit must turn into a health detriment. How else can they explain why their associations are the opposite of what the experimental trials conclude? And if they don’t explain this away somehow, they might have to acknowledge that they’ve been doing pseudoscience for their entire careers. And maybe they’re right, but I certainly wouldn’t bet my life on it.

Ultimately we’re left with a decision about what we’re going to believe: the observations, or the experiments designed to test those observations. Good scientists will always tell you to believe the experiments. That’s why they do them.

Egregious (and embarrassing) error correction: In an early version of the post, I suggested that if you read the chapter on nutritional epidemiology in the textbook Modern Epidemiology, you’d see that the best epidemiologists agree that this pursuit is pathological. A reader from my institution — a UC Berkeley grad student — pointed out that the chapter on nutritional epi in the textbook was actually written by Walter Willett and that, not surprisingly, it does not agree with this position. Here’s how Willett ends that chapter:

The last two decades have seen enormous progress in the development of nutritional epidemiology methods. Work by many investigators has provided clear support for the essential underpinnings of this field. Substantial between-person variation in consumption of most dietary factors in populations has been demonstrated, methods to measure diet applicable to epidemiologic studies have been developed, and their validity has been documented. Based on this evidence, many large prospective cohort studies have been established that are providing a wealth of data on many outcomes that will be reported during the next decade. In addition, methods to account for errors in measurement of dietary intake have been developed and are beginning to be applied in reporting findings from studies of diet and disease.

Nutritional epidemiology has contributed importantly to understanding the etiology of many diseases. Low intake of fruits and vegetables has been shown to be related to increased risk of cardiovascular disease. Also, a substantial amount of epidemiologic evidence has accumulated indicating that replacing saturated and trans fats with unsaturated fats can play an important role in the prevention of coronary heart disease and type 2 diabetes. Many diseases—as diverse as cataracts, neural-tube defects, and macular degeneration—that were not thought to be nutritionally related have been found to have important dietary determinants. Nonetheless, much more needs to be learned regarding other diet and disease relations, and the dimensions of time and ranges of dietary intakes need to be expanded further. Furthermore, new products are constantly being introduced into the food supply, which will require continued epidemiologic vigilance.

The development and evaluation of additional methods to measure dietary factors, particularly those using biochemical methods to assess long-term intake, can contribute substantially to improvements in the capacity to assess diet and disease relations. Also, the capacity to identify those persons at genetically increased risk of disease will allow the study of gene–nutrient interactions that are almost sure to exist. The challenges posed by the complexities of nutritional exposures are likely to spur methodologic developments. Such developments have already occurred with respect to measurement error. The insights gained will have benefits throughout the field of epidemiology.

Now the reason I made this mistake is because I was rushing (no excuse, despite the warning up front) and so working from memory about a chapter that the UCLA epidemiologist Sander Greenland, one of the editor/authors of Modern Epidemiology, sent me when I was writing the New York Times Magazine article in 2007. The chapter Greenland was discussing and that he had sent me at the time was one he had authored, chapter 19 — “Bias Analysis” — and it was discussing observational epidemiology in general.

Here’s Greenland on the problem with all these studies — nutritional epi included — and how they’re interpreted:

Conventional methods assume all errors are random and that any modeling assumptions (such as homogeneity) are correct. With these assumptions, all uncertainty about the impact of errors on estimates is subsumed within conventional standard deviations for the estimates (standard errors), such as those given in earlier chapters (which assume no measurement error), and any discrepancy between an observed association and the target effect may be attributed to chance alone. When the assumptions are incorrect, however, the logical foundation for conventional statistical methods is absent, and those methods may yield highly misleading inferences. Epidemiologists recognize the possibility of incorrect assumptions in conventional analyses when they talk of residual confounding (from nonrandom exposure assignment), selection bias (from nonrandom subject selection), and information bias (from imperfect measurement). These biases rarely receive quantitative analysis, a situation that is understandable given that the analysis requires specifying values (such as amount of selection bias) for which little or no data may be available. An unfortunate consequence of this lack of quantification is the switch in focus to those aspects of error that are more readily quantified, namely the random components.

Systematic errors can be and often are larger than random errors, and failure to appreciate their impact is potentially disastrous. The problem is magnified in large studies and pooling projects, for in those studies the large size reduces the amount of random error, and as a result the random error may be but a small component of total error. In such studies, a focus on “statistical significance” or even on confidence limits may amount to nothing more than a decision to focus on artifacts of systematic error as if they reflected a real causal effect.

]]>Last week, I tweeted a New England Journal of Medicine image challenge, part of the journal’s continuing education program for physicians. I suggested that it might be a source of comfort to those who were worried about the insulin hypothesis as a viable hypothesis to explain obesity and excess fat accumulation. Although I linked to the NEJM page and the link worked for me, I gather some who tried to click on it were presented with other image challenges and were wondering, for instance, why I cared if they could diagnose a pneumothorax when they saw one. So here’s the image challenge I had in mind, and the correct response is below. The relevance should be reasonably obvious.